Hydrological Modelling of Narmada basin in Central India using Soil and Water Assessment Tool (SWAT)

T. Thomas, N. C. Ghosh, K. P. Sudheer

National Institute of Hydrology, Roorkee (A Govt. of India Society under Ministry of Water Resources, RD & GR, Govt. of India)

Presentation Outline

• Introduction

• Objectives

• Study area

• Methodology

• Climate change detection in historical time horizons

• Hydrologic Modelling

• Impact assessment for future time horizons

Historical and Present time horizon:

Warming is Unequivocal and Unprecedented (IPCC).

Water is one of the sectors going to be impacted.

Future time horizons:

Global temperature change is likely to exceed 1.5°C for the end

of the 21st century relative to 1850.

Expected to have significant impacts on the hydrological

cycle.

Introduction

• Application of climate change science remains a challenge.

– Heavy analytical procedures

– Limited attention to implementation

– High level of uncertainty

– Political dimensions

• Indian sub-continent: combination of

– climate change

– rapid population growth

– land use change

– adaptation mechanisms

• Hydrologic modelling can be a useful tool for scenario analysis for

water resources planning

Identification of climate change signals in Narmada basin through statistical analysis of historical climate variables.

Modelling the watershed hydrology in Narmada basin by a

physically based large scale hydrologic model.

Assessment of the impact of climate change using projected

climate scenarios (AR5) with emphasis on future water

availability including extreme events.

Objectives

Objectives

• Catchment area: 98796 sq. km.

• Non-snowfed river basin

• Sustained by base flows

• West flowing river

• Lies in the States of MP, Gujarat,

• Maharashtra and Chhattisgarh

• Average annual rainfall: 1178 mm

Study Area

• 30 major & 150 medium projects planned in Narmada basin.

• Sustainability of the existing and upcoming projects uncertain.

• Hydrological impact assessments are much more imperative

now.

– Non snow-fed perennial basin

– Sustained only by base flow

– Change in water balance components

• Predictions of hydrologic variables in future under climate

change.

• Research gaps:

– impact assessment in data scarce areas

– Uncertainty issues,

– Non-stationarity issues.

Methodology

Observed Climatic Variables AND Hydrologic Variables (Precipitation, Temperature, Stream flows)

AR5 RCM Climate Variables & Downscaled Climate Data for 2 RCP’s and 4 time horizons

Multi-site Calibration & Validation SWAT Model after incorporating existing dams

Climate change impact assessments (existing dams only & with proposed and existing dams)

SWAT Run with default parameter values

LH-OAT for Sensitivity Analysis

Establish Initial Parameter Ranges

LH-sampling to assign 1000 groups of parameter values

B Behavioural simulations > 80% ?

Select parameter values with corresponding NS greater than 0.50

Use parameter range of last iteration as input range of next iteration

Yes

No

Detection of Climate Change Signals in Present Climate

TESTED SWAT MODEL

Forecasted hydrological variables with existing and proposed dams

Water availability, Flood, Drought Assessments (Indices based)

Addition of proposed dams in the basin

Bias Correction of the Downscaled Data (QM)

Forecasted hydrological variables with existing dams

Extreme rainfall: Increased in recent times

• 1-day maximum P: Significant positive trend (z = 3.66)

Particulars 1951-70 1989-08

1-day rainfall > 300 mm 1 9

1-day rainfall > 200 mm 8 17

Average 1-day maximum rainfall 203.9 mm 275.9 mm

Temporal variation of amount of 1-day maximum rainfall in the basin (MK test z statistic = 3.66).

Frequency analysis: Increase in n-yr return period rainfall

Grid cells with significant increasing trends in 5-year return period rainfall

Droughts: Increased in recent times

• Drought frequency more pronounced in the middle zone.

• Increase of drought in upper zone a cause of concern.

SPI based drought duration for various zones

Extreme Temperatures: Increasing continuously

• 1-day maximum temperature : increasing @ 1.10°C/100 yr.

(more than global average rate).

• 1-day minimum temperature: increasing @ 3.20°C/100 yr.

S.

No.

Zones of

basin

Mann-Kendall test statistic & inference

1969-88 1990-2009

1. Upper zone +1.72 (not significant) +1.01 (not significant)

2. Middle zone +1.24 (not significant) +1.29 (not significant)

3. Lower zone +2.31 (significant) +2.11 (significant)

Hydrologic Modelling using SWAT

Features of SWAT

• Physically based distributed model

• Continuous time model

• Long term yield model

• Uses readily available data

• Can be used for long term impact studies

Processes Modelled

• Weather, Hydrology, Sedimentation

• Plant growth, Nutrient cycling

• Pesticide dynamics, Bacteria

• Management

SWAT setup for the Narmada up to Barmanghat

• SWAT model setup for the Narmada upto Barmanghat (26000 sq. km).

• One Major Multi-purpose Project (Bargi Dam) for irrigation, power

generation, industrial and domestic use.

• 10 projects being planned in the catchment : Rosra, Basania, Halon,

Upper Narmada, Upper Burhner, Raghavpur, Atariya, Machhrewa,

Sher and Chinki.

• Virgin simulation of the model has been carried out before setting up of

reservoirs.

• Default run of model Bargi Reservoir (properties and inflows).

.

DEFAULT RUN: VIRGIN AND BARGI DAM

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Jan

-88

Jun

-88

No

v-8

8

Ap

r-8

9

Sep

-89

Feb

-90

Jul-

90

De

c-9

0

May

-91

Oct

-91

Mar

-92

Au

g-9

2

Jan

-93

Jun

-93

No

v-9

3

Ap

r-9

4

Sep

-94

Feb

-95

Jul-

95

De

c-9

5

May

-96

Oct

-96

Mar

-97

Au

g-9

7

Jan

-98

Jun

-98

No

v-9

8

Ap

r-9

9

Sep

-99

Feb

-00

Jul-

00

De

c-0

0

Mo

nth

ly d

isch

arge

(cu

me

cs)

Calibrated (1988-00)

Virgin (1988-00)

• Sources of irrigation allocated: based on the actual water use of

reservoir water in command areas & groundwater in non-command

areas.

SWAT Parameters for Stream flow simulation

1. Base flow recession coefficient: ALPHA_BF

2. GW Delay time : GW_DELAY

3. Revap coefficient : GW_REVAP

4. Soil evaporation coefficient : ESCO

5. Available Water Content : SOL_AWC

6. Saturated hydr. Conductivity : SOL_K

7. Manning’s n : OV-N

7. Curve number : CN_f

8. Slope : SLOPE

9. Manning’s n for channel : CH_N

10. Water depth (shallow aq.) :GWQMN

11. Slope length of main channel : CH_S

12. Snowfall temperature : SFTMP

13. Slope of sub-basin : SLSUBBSN

14. Surface lag : SURLAG

• One at a time (LH-OAT) Sensitivity Analysis has been performed

for the parameters responsible for stream flow simulation.

Multi-site SWAT Calibration

• Multi-site calibration of SWAT model using 6 gauging sites

(Dindori, Mohgaon, Manot, Belkheri, Patan and Barmanghat)

• Calibration: 1988-00, Validation: 2001-05

• Sensitivity Analysis has been performed for the parameters

responsible for stream flow simulation.

• CN2, SOL_AWC, ESCO, GW_REVAP, ALPHA_BF and

GW_DELAY.

0

100

200

300

400

500

600

700

800

900

Jan

-88

Jun

-88

No

v-8

8

Ap

r-8

9

Sep

-89

Feb

-90

Jul-

90

Dec

-90

May

-91

Oct

-91

Mar

-92

Au

g-9

2

Jan

-93

Jun

-93

No

v-9

3

Ap

r-9

4

Sep

-94

Feb

-95

Jul-

95

Dec

-95

May

-96

Oct

-96

Mar

-97

Au

g-9

7

Jan

-98

Jun

-98

No

v-9

8

Ap

r-9

9

Sep

-99

Feb

-00

Jul-

00

Dec

-00

Mo

nth

ly d

isch

arge

(cu

me

cs)

Month/Year

Observed_1980-00

Calibrated (1984-00)

0

50

100

150

200

250

300

350

400

450

500

Jan

-88

Jun

-88

No

v-8

8

Ap

r-8

9

Sep

-89

Feb

-90

Jul-

90

Dec

-90

May

-91

Oct

-91

Mar

-92

Au

g-9

2

Jan

-93

Jun

-93

No

v-9

3

Ap

r-9

4

Sep

-94

Feb

-95

Jul-

95

Dec

-95

May

-96

Oct

-96

Mar

-97

Au

g-9

7

Jan

-98

Jun

-98

No

v-9

8

Ap

r-9

9

Sep

-99

Feb

-00

Jul-

00

Dec

-00

Mo

nth

ly d

isch

arge

(cu

me

cs)

Month/Year

Observed_1980-00

Calibrated (1984-00)

Simulated flows at Patan

Simulated flows at Dindori

Model calibration

0

50

100

150

200

250

300

Jan

-88

Jun

-88

No

v-8

8

Ap

r-8

9

Sep

-89

Feb

-90

Jul-

90

Dec

-90

May

-91

Oct

-91

Mar

-92

Au

g-9

2

Jan

-93

Jun

-93

No

v-9

3

Ap

r-9

4

Sep

-94

Feb

-95

Jul-

95

Dec

-95

May

-96

Oct

-96

Mar

-97

Au

g-9

7

Jan

-98

Jun

-98

No

v-9

8

Ap

r-9

9

Sep

-99

Feb

-00

Jul-

00

Dec

-00

Mo

nth

ly d

isch

arge

(cu

me

cs)

Month/Year

Observed_1980-00

Calibrated (1984-00)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Jan

-88

Jun

-88

No

v-8

8

Ap

r-8

9

Sep

-89

Feb

-90

Jul-

90

Dec

-90

May

-91

Oct

-91

Mar

-92

Au

g-9

2

Jan

-93

Jun

-93

No

v-9

3

Ap

r-9

4

Sep

-94

Feb

-95

Jul-

95

Dec

-95

May

-96

Oct

-96

Mar

-97

Au

g-9

7

Jan

-98

Jun

-98

No

v-9

8

Ap

r-9

9

Sep

-99

Feb

-00

Jul-

00

Dec

-00

Mo

nth

ly d

isch

arge

(cu

me

cs)

Month/Year

Observed_1980-00

Calibrated (1988-00)

Simulated flows at at

Belkheri

Simulated flows at

Barmanghat

Observed & Simulated Hydrographs at Barmanghat

0

500

1000

1500

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3000

3500

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4500

Ja

n-8

8

Ju

l-88

Ja

n-8

9

Ju

l-89

Ja

n-9

0

Jul-90

Ja

n-9

1

Ju

l-91

Ja

n-9

2

Ju

l-92

Ja

n-9

3

Ju

l-93

Ja

n-9

4

Ju

l-94

Ja

n-9

5

Ju

l-95

Ja

n-9

6

Ju

l-96

Jan-9

7

Ju

l-97

Ja

n-9

8

Ju

l-98

Ja

n-9

9

Ju

l-99

Ja

n-0

0

Ju

l-00

Dis

charg

e (

cum

ecs) Observed

Simulated

0

500

1000

1500

2000

2500

3000

3500

Ja

n-0

1

Ap

r-01

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l-01

Oct-

01

Ja

n-0

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r-02

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l-02

Oct-

02

Ja

n-0

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r-03

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l-03

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n-0

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r-04

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l-04

Oct-

04

Ja

n-0

5

Ap

r-05

Ju

l-05

Oct-

05

Dis

ch

arg

e (

cu

me

cs) Observed

Simulated

Model performance

S.

No.

Name of the

gauging site

Calibration Validation

NSE RSR PBias NSE RSR PBias

1. Dindori 0.85 0.39 -7.48 0.85 0.32 -4.56

2. Mohgaon 0.35 0.81 -37.60 0.90 0.28 -5.05

3. Manot 0.95 0.23 -8.91 0.97 0.14 -1.57

4. Patan 0.63 0.61 -50.45 0.68 0.51 -42.59

5. Belkheri 0.90 0.32 -5.80 0.70 0.48 -18.61

6. Barmanghat 0.79 0.46 13.93 0.79 0.28 8.33

Climate Change Impact Assessments

Global Climate Models

(GCM)

Representation Concentration

Pathways (RCPs)

Downscaling

Regional Climate

Models (RCMs)

Bias Correction

(Quantile mapping)

Calibrated and

Validated SWAT Model

Multiple outputs of

streamflow

Climate Models, RCPs and Extreme Events Indices

• 6 RCMs (0.50o x 0.50o)

• CCSM4

• CNRM-CM5

• GFDL-CM3

• ACCESS1.0

• MPI-ESM-L

• NOR-ESM-M

4 time-horizons

1970-05 (historical)

2006-40 (near term)

2041-70 (mid term)

2071-99 (end term)

2 RCP Scenarios

RCP4.5

RCP8.5

S. Index Rainfall range

1. Wet day > 2.50 mm

2. Heavy rainfall day 100 mm - 200 mm

3. Very heavy rainfall day > 200 mm

Source: European Climate Assessment & Dataset (ECA&D)

Sl. # Index Range

1. Extremely hot days MaxT > 45oC

2. Summer days MaxT > 40oC

3. Warmer Days MaxT > 35oC

4. Summer nights MinT > 25oC

Scenario Analysis: Higher rainfall variability

0 200 400 600 800 1000 1200 1400 1600 1800

2006

2009

2012

2015

2018

2021

2024

2027

2030

2033

2036

2039

RCM-Mean average annual rainfall (mm)

Year

0 200 400 600 800 1000 1200 1400 1600 1800

2041

2044

2047

2050

2053

2056

2059

2062

2065

2068

RCM-Mean average annual rainfall (mm)

Year

0 200 400 600 800 1000 1200 1400 1600 1800

2071

2074

2077

2080

2083

2086

2089

2092

2095

2098

RCM-Mean average annual rainfall (mm)

Year

RCM mean average annual rainfall under RCP4.5

Future scenario : Higher extreme rainfall events

0

50

100

150

200

250

300

350

400

450

ACCESS1.0 CCSM4 CNRM-CM5 GFDL-CM3 MPI-ESM-LR Nor-ESM-M RCM-Mean

1-d

ay m

axim

um

rain

fall

(mm

)

2006-40 2041-70 2071-99

Future 1-day maximum rainfall

Future scenario: Higher extreme temperatures

The summer days, warmer days as well as the summer nights are projected

to increase in all future time horizons

RCM Time

horizon

RCP4.5 RCP8.5

RCM

mean

2006-40 1 in 3.50 1 in 2.69

2041-70 1 in 2.73 1 in 2.61

2071-99 1 in 2.60 1 in 2.26

Extremely hot days

Time

horizon

Maximum of MaxT

(oC)

Minimum of MaxT

(oC)

Scenario RCP4.5 RCP8.5 RCP4.5 RCP8.5

2006-40 45.67 46.06 21.84 22.40

2041-70 45.64 46.20 21.13 21.43

2071-99 46.22 46.65 22.28 22.78

Average daily maximum temperature : CCSM4

Future scenario: Lower water availability

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

5 10 20 50 75 90 95

Annu

al de

pend

able

flow

(cum

ecs)

Probability of Exceedance (%)

HISTORICAL SIMULATEDACCESS1.0CCSM4CNRM-CM5GFDL-CM3MPI-ESM-LRNOR-ESM-M

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

5 10 20 50 75 90 95

Annu

al de

pend

able

flow

(cum

ecs)

Probability of exceedance (%)

HISTORICAL SIMULATEDACCESS1.0CCSM4CNRM-CM5GFDL-CM3MPI-ESM-LRNOR-ESM-M

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

5 10 20 50 75 90 95

Annu

al de

pend

able

flow

(cum

ecs)

Probability of exceedance (%)

HISTORICAL SIMULATEDACCESS1.0CCSM4CNRM-CM5GFDL-CM3MPI-ESM-LRNOR-ESM-M

Future scenario: More surface water droughts

-1.50

-1.00

-0.50

0.00

0.50

1.00

20

06

20

10

20

14

20

18

20

22

20

26

20

30

20

34

20

38

20

42

20

46

20

50

20

54

20

58

20

62

20

66

20

70

20

74

20

78

20

82

20

86

20

90

20

94

20

98

3m

onth

-SD

I (J

une t

o A

ugu

st)

Hydrological drought scenario at Barmanghat (RCP4.5)

Future scenario: More groundwater droughts

-3

-2

-1

0

1

2

3

4 Ju

n-7

1

Ju

l-72

Aug-7

3

Sep-7

4

Oct-

75

No

v-7

6

De

c-7

7

Jan-7

9

Fe

b-8

0

Ma

r-8

1

Apr-

82

Ma

y-8

3

Ju

n-8

4

Ju

l-85

Aug-8

6

Sep-8

7

Oct-

88

No

v-8

9

Dec-9

0

Ja

n-9

2

Fe

b-9

3

Ma

r-9

4

Apr-

95

Ma

y-9

6

Ju

n-9

7

Ju

l-98

Aug-9

9

12

m-S

PI

Groundwater Drought Scenario RCP8.5

Increased frequency of dry and wet events

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1988-05 2006-40 2041-70 2071-99

# of

eve

nts/

year

Extremely dry

Severely dry

Moderately dry

(a) dry events

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1988-05 2006-40 2041-70 2071-99

# of

eve

nts/

year

Extremely wet

Severely wet

Moderately wet

(b) wet events RCP8.5 RCP8.5

Conclusions SWAT can be effectively used for hydrologic modelling of large river basins

and used effectively for scenario analysis.

Increasing maximum and minimum temperature during the historical and

all future time-horizons.

Higher extreme rainfall events in the recent and future time-horizons.

Lower water availability during all future time-horizons.

Magnitude of low flows and high flows to decrease in future time-horizons.

More frequent droughts in all future time-horizons.

Simultaneous occurrences of droughts and wet events with changing

rainfall pattern with frequent dry spells calls for optimal water utilisation.

Supply-side management of the available water coupled with demand-side

management can be considered as a viable climate change adaptation tool